Spain

Digital discrimination is a form of discrimination whereby users are automatically treated unfairly, unethically or just differently based on their personal data by a machine learning (ML) system. Examples of digital discrimination include low-income neighbourhood’s targeted with high-interest loans or low credit scores, and women being undervalued by 21% in online marketing. Recently, different techniques and tools have been proposed to detect biases that may lead to digital discrimination. These tools often require technical expertise to be executed and for their results to be interpreted.  To allow non-technical users to benefit from ML, simpler notions and concepts to represent and reason about digital discrimination are needed. In this paper, we use norms as an abstraction to represent different situations that may lead to digital discrimination. In particular, we formalise non-discrimination norms in the context of ML systems and propose an algorithm to check whether ML systems violate these norms.

ECAI 2020

A Normative Approach to Attest Digital Discrimination

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Companies have considered adoption of various high-level artificial intelligence (AI) principles for responsible AI, but there is less clarity on how to implement these principles as organizational practices. This paper reviews the principles-to-practices gap. We outline five explanations for this gap ranging from a disciplinary divide to an overabundance of tools. While some proposed approaches to responsible AI emphasize attention to a subset of ethical issues primarily in the design stage, this paper presents the measurement of real-world impacts of AI systems as a necessary shift in thinking. We argue that an impact assessment framework that is broad, operationalizable, flexible, iterative, guided, and participatory is a promising approach to close the principles-to-practices gap. To help practitioners with applying these recommendations, we review a case study of AI's use in forest ecosystem restoration, demonstrating how an impact assessment framework can translate into effective and responsible AI practices.

Principles to Practices for Responsible AI: Closing the Gap

Farmer suicide is a significant issue in several countries. This issue stems from different factors such as farmers’ inability to sell their products at the desired profit level. To help farmers get a better understanding of the future market condition, this study attempts to build a predictive model to foresee future crop price trends in various markets using a deep learning approach.

Ameliorating Farmer Suicides by Predicting Crop Price Trends using a Deep Learning Approach

"Fair inference in supervised learning is an important and active area of research, yielding a range of useful methods to assess and account for fairness criteria
when predicting ground truth targets. As shown in recent work, however, when target labels are error prone, potential prediction unfairness can arise from measurement error. In this paper, we show that, when an error-prone proxy target
is used, existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest. To remedy this problem, we suggest a framework resulting from the combination of two existing literatures: fair ML
methods, such as those found in the counterfactual fairness literature on the one hand, and, on the other, measurement models found in the statistical literature. We discuss these approaches and their connection resulting in our framework.
In a healthcare decision problem, we find that using a latent variable model to account for measurement error removes the unfairness detected previously."

Fair Inference on Error-Prone Outcomes

Information is key during a crisis such as the current COVID-19 pandemic as it greatly shapes people’s opinion, behaviour and even their psychological state. It has been acknowledged from the Secretary-General of the United Nations that the infodemic of misinformation is an important secondary crisis produced by the pandemic. Infodemics can amplify the real negative consequences of the pandemic in different dimensions: social, economic and even sanitary. For instance, infodemics can lead to hatred between population groups that fragment the society influencing its response or result in negative habits that help the pandemic propagate. On the contrary, reliable and trustful information along with messages of hope and solidarity can be used to control the pandemic, build safety nets and help promote resilience and antifragility. We propose a framework to characterize leaders in Twitter based on the analysis of the social graph derived from the activity in this social network. Centrality metrics are used to identify relevant nodes that are further characterized in terms of users’ parameters managed by Twitter. We then assess the resulting topology of clusters of leaders. Although this tool may be used for surveillance of individuals, we propose it as the basis for a constructive application to empower users with a positive influence in the collective behaviour of the network and the propagation of information.

Characterizing social leaders on Twitter during COVID-19 crisis 

"In this short article, we argue that the SDGs should figure more prominently in marking out the route to realising the potential of A/IS, while at the same time recognizing the need for a critical appraisal. We discuss how increasing awareness of an interdisciplinary research field “A/IS for development”
(A/IS4D) could contribute to achieving this, where by this term, we refer to a field that deals with the distinctive challenges posed by
A/IS technologies, particularly for more vulnerable and marginalized populations, while integrating a development studies, human-rights
centered perspective. We contend that such a perspective, and more specifically, the measuring of impact in terms of appropriate SDGs, is critical
to avoiding possible adverse effects arising from the introduction of such a disruptive technology without sufficient reflection, a real and
present danger given the current techno-optimist zeitgeist and the enormous power of the Information and Communication Technologies (ICT)
corporate sector. Unfortunately, the academic research literature of this field, like that of the more general and well-established ICT4D field, remains marginal, with contributions having little representation
in high-impact conferences and journals. This short article constitutes a modest attempt to contribute to the dissemination of this important
topic among researchers and professionals in the A/IS sector, in the way of a call for action.  "

AI4Eq: for a True Global Village not for Global Pillage

"In this article, we report on preliminary research on the
potential contributions of Artificial Intelligence to measure the social impact of social enterprises. We describe SIAMES, a recommender system of social impact indicators. The system has two phases: First, it tries to extract structured information from a corpus of impact measurement reports through ontology-based Semantic Text Mining.
Second, drawing inspiration from Case-Based Reasoning, it retrieves the most appropriate indicators for each social enterprise. In this brief
work, we outline the system and indicate the lines of research carried out to date. We also discuss some preliminary results and present the
planned next steps."

SIAMES - Social Impact Advisor and Measurement System

"The transformation to the Digital Society presents a challenge to engineer ever more complex socio-technical and cyber-physical systems. However, it is essential that these systems are designed with respect not just to conventional functional and non-functional requirements, but also with respect to satisfying qualitative human values, and assessing their impact on global challenges, such as those expressed
by the UN sustainable development goals (SDGs). In this presentation, we present a set of design principles and an associated meta-platform which formalise and codify deep social knowledge, as a basis for solving wicked societal problems and for contributing to the achievement of SDGs. The ideas are illustrated through the design of a collective action app, for plastic re-use and reducing plastic waste, called Urban Refill.  As with the inter-connectedness of the SDGs, we conclude by arguing that the inter-connectedness of the Digital Society implies that system development cannot be undertaken in isolation from other systems.
"

No App is an Island: Collective Action and Sustainable Development Goal-Sensitive Design

"Nowadays, the buildings have an important role in the high demand for electrical energy. Therefore, the energy management of the buildings may have significant influence on reducing the electricity consumption. Moreover, Electric Vehicles (EVs) have been considering as a power storage device in Smart Buildings (SBs)
aiming to reduce the cost and consuming energy. Here, an energy management framework is proposed in which by considering the flexibility of the contracted power of each apartment, an optimal charging-discharging scheduled for EVs and Battery Energy Storage System (BESS) is defined over a long time period to minimize the electricity cost of the building. The proposed model is designed by
a Mixed Binary Linear Programming formulation (MBLP) that the charging and discharging of EVs as well as BESS in each period is treated as binary decision variables. In order to validate the model, a case study involving three scenarios are considered. The obtained results indicate a 15% reduction in total electricity consumption cost and consumption energy by the grid over one year. Finally, the impact of capacity and charge/discharge rate of BESS on the power cost is analyzed and the optimal size of the BESS for assumed SB in the case study is also reported."

Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix BinaryLinear Programming model 

Power grid data are going “big” with the deployment of various sensors. The big data in power grids create huge opportunities for applying artificial intelligence technologies to improve resilience and reliability. This presentation introduces multiple real-world applications based on artificial intelligence and big data to improve power grid situational awareness and resilience. These applications include event detection, inertia estimation, event location and magnitude estimation, data authentication, control, and stability assessment. These applications are operating on a real-world system called FNET/GridEye, which is a wide-area measurement network and arguably the world-largest cyber-physical system that collects power grid big data. These applications showed much better performance compared with conventional approaches, and accomplished new tasks that are impossible to realized using conventional technologies. These encouraging results demonstrate that combining power grid big data and artificial intelligence can uncover and capture the non-linear correlation between power grid data and its stability indices, and will potentially enable many advanced applications that can significantly improve power grid resilience. 

Build Smart Grids on Big Data and Artificial Intelligence — A Real-world Example 

"Utility companies have a great interest in identifying fraudulent
activity such as Non-Technical Losses (NTL). Typically, they resort
to machine learning solutions in order to automate the identifica-
tion of such losses. In this paper, we extend and improve an NTL-
detection framework: by including knowledge-based NTL segmen-
tation, we are able to not only provide more accurate predictions but
also to include better explanations for them. Our improved models
focus on specific types of NTL and, therefore, the explanations pro-
vided are easier to interpret, allowing the Stakeholders to make more
informed decisions. Our improvements and results may benefit other
industrial frameworks."

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